# Staff AI Platform Engineer: Agent & Retrieval Infrastructure at Bedrock Ocean Exploration

- Company: Bedrock Ocean Exploration
- What the company does: Bedrock Ocean Exploration is a platform for underwater vehicles. Their technology allows for the collection of seafloor data and makes ocean mapping safer, with minimal environmental impact, risk of endangered animal takes, and infrastructure damage. Backed by Eniac Ventures.
- Company website: https://bedrockocean.com
- Type: Startups (AI role)
- Level: Senior
- Location: Remote
- Work setup: Remote
- Pay: $160K to $220K base salary per year (USD)
- Posted: 2026-09-13
- Apply by: 2026-10-28
- Apply: https://jobs.ashbyhq.com/bedrockocean/ecfac75e-1d15-4b4a-aa9d-baa200f2341c
- Page: https://www.1752.vc/careers/jobs/bedrock-ocean-exploration-staff-ai-platform-engineer-agent-and-retrieval-infrast/

## About the role

We are looking for a Staff Platform Engineer to lead our AI architecture. This role goes beyond building agents on existing platforms; you will create the infrastructure itself, including the orchestration layer, the data and retrieval pipeline, and the security model required to work with production data. You will also build the tools and abstractions that allow our engineering team to implement AI features independently.

## What they're looking for

- 8+ years in software and infrastructure engineering, including deep production backend experience (Python or TypeScript preferred, Go fine) and staff-level ownership of technical direction
- Hands-on experience standing up Amazon Bedrock in production: agents, knowledge bases, guardrails, model access, and the throughput and quota decisions that come with them
- Containerized service deployment on ECS, EKS, or Lambda, with CI/CD you have owned rather than inherited. The models are managed, but the backend APIs, tool endpoints, and ingestion jobs still run somewhere real
- Practical RAG and vector search experience: embeddings, chunking strategies, semantic search quality, and operating a managed vector database (OpenSearch Serverless, Pinecone, pgvector, or similar) at production scale and cost
- Real data engineering: you have built or substantially extended ingestion pipelines over messy, heterogeneous, unstructured sources, and you think about freshness and correctness as SLAs rather than afterthoughts
- Strong AWS ecosystem expertise: IAM roles and least privilege for machine identities, VPC networking and PrivateLink, Lambda, S3, KMS, CloudWatch, and provisioning safely through infrastructure as code (Terraform, CDK, or CloudFormation)

Tags: Software Team